Stage level, volume, and time-frequency information content of Lake Tana using stochastic and wavelet analysis methods

Abstract. Lake Tana is the largest fresh water body situated in the north western highlands of Ethiopia. It serves for local transport, electric power generation, fishing, ecological restoration, recreational purposes, and dry season irrigation supply. Evidence show, the lake has dried at least once at about 15 000–17 000 BP (before present) due to a combination of high evaporation and low precipitation events. Past attempts to observe historical fluctuation of Lake Tana based on simplistic water balance approach of inflow, out-flow and storage have failed to capture well known events of drawdown and rise of the lake that have happened in the last 44 years. This study is aimed at simulating the lake level, specifically extreme events of the lake variation using stochastic approaches. Fourty-four years of daily, monthly and mean annual lake level data has showed a Gaussian variation with goodness of fit at 0.01 significant levels of the Konglomorov-Simrnov test. Three stochastic methods were employed, namely perturbations approach, Monte-Carlo methods and wavelet analysis, and the results were compared with the stage level measurements. The stochastic simulations predicted the lake stage level of the 1972, 1984 and 2002/2003 historical droughts 99% of the time. The information content (frequency) of fluctuation of Lake Tana for various periods was resolved using Wigner's Time-Frequency Decomposition method. The wavelet analysis agreed with the perturbations and Monte Carlo simulations resolving the time (1970s, 1980s and 2000s) in which low frequency and high spectral power fluctuation has occurred. In summary, the Monte-Carlo and perturbations methods have shown their superiority for risk analysis over deterministic methods while wavelet analysis has met reconstructing stage level historical record at multiple time scales. A further study is recommended on dynamic forecasting of the Lake Tana stage level using a combined approach of the perturbation and wavelet analysis methods.